Survey biomass

Comparing model outputs of survey biomass.

Code

# load ----
library(dplyr)
library(ggplot2)
library(plotly)
theme_set(theme_bw())

# data ----
# obs data 
source(here::here('r', 'bridge_data.r'))
# admb model output
admb = read.csv(here::here('data', 'admb', 'survey.csv')) 
# rtmb model output
report1 = readRDS(here::here('data', 'rtmb', 'report1.rds'))

data.frame(year = srv_yrs,
           biomass = srv_obs,
           sd_2022 = srv_sd,
           rtmb_2022 = report1$srv_pred,
           admb_2022 = admb$pred) %>% 
  mutate(lci_2022 = biomass - 1.96 * sd_2022,
         uci_2022 = biomass + 1.96 * sd_2022) %>% 
  tidyr::pivot_longer(c(rtmb_2022, admb_2022), names_to='model') %>% 
ggplot(aes(year, biomass)) +
  geom_point(color='gray') +
  geom_errorbar(aes(ymin = lci_2022, ymax = uci_2022), width=.2, color='gray') +
  expand_limits(y=0) +
  scale_y_continuous(labels = scales::comma) +
  geom_line(aes(y = value, color = model)) +
  scico::scale_color_scico_d(palette = 'roma') +
  xlab('Year') + 
  ylab('Biomass (t)') -> p1

ggplotly(p1)

Figure 1. Survey biomass inputs (VAST-based w/lognormal error structure) and confidence intervals (gray). Model outputs are shown for the 2022 ADMB and RTMB models.

Filtering figures

One can click on the model names in the legend to add/remove from the figure.